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Supervised Fine Tuning of CodeLlama-13B for Text to Cypher Generation"],"metadata":{"id":"ukHw1p6lGfA6"}},{"cell_type":"markdown","source":["# Workspace Setup"],"metadata":{"id":"Qtqu5ViIKo1w"}},{"cell_type":"markdown","source":["We install core dependencies with their suggested versions as follows. These are based on working on a single GPU with ampere architecture such as A100 40GB."],"metadata":{"id":"2phbp1R-A6bc"}},{"cell_type":"code","source":["#@title Install Core Dependencies\n","\n","!pip install -U accelerate          # Simplifies running PyTorch models on multi-GPU setups.\n","!pip install -U bitsandbytes        # Handles model quantization.\n","!pip install -U datasets            # Utilities to access and handle HF datasets.\n","!pip install -U einops              # Enables efficient manipulation of tensor operations.\n","!pip install -U evaluate            # Framework for evaluating model performance.\n","!pip install -U ninja               # Speeds up building C++ applications.\n","!pip install -U packaging           # Core utilities for package version management.\n","!pip install -U peft                # Parameter efficient fine-tuning, used to implement LoRA and QLoRA.\n","!pip install -U tensorboard         # Package to visualize metrics and monitor model training and performance.\n","!pip install -U torch               # Core library for deep learning.\n","!pip install -U transformers        # Tools and utilities to work with transformer based models.\n","!pip install -U trl                 # Toolkit for reinforcement learning with transformers."],"metadata":{"id":"FyNqvKsbAC9I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Find torch Version Installed in Google Colab\n","\n","import torch\n","print(torch.__version__)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D-gbs3Qru-Wz","executionInfo":{"status":"ok","timestamp":1716428855331,"user_tz":360,"elapsed":1751,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"d3e34347-e12a-46b7-a069-2708bd6043ec"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["2.3.0+cu121\n"]}]},{"cell_type":"code","source":["#@title Install Flash Attention\n","\n","# Limit the number of jobs to accomodate the compute capabilities\n","%env MAX_JOBS=2 # for Google Colab\n","\n","# Install flash attention - for Ampere GPUs\n","!pip install -U flash-attn --no-build-isolation"],"metadata":{"id":"GbCKU3zOArrF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Resources Estimation\n","\n","import torch\n","gpu_stats = torch.cuda.get_device_properties(0)\n","start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n","max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n","print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n","print(f\"{start_gpu_memory} GB of memory reserved.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FyTG3B-GFSlC","executionInfo":{"status":"ok","timestamp":1716428943393,"user_tz":360,"elapsed":92,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"5f262e40-b7cd-4bc1-fe45-04f017b6a33f"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.564 GB.\n","0.0 GB of memory reserved.\n"]}]},{"cell_type":"code","source":["#@title Google Colab Drive Helper\n","\n","# For Google Colab settings\n","from google.colab import userdata, drive\n","\n","# This will prompt for authorization\n","drive.mount('/content/drive')\n","\n","# Set the working directory\n","%cd '/content/drive/MyDrive/finetuneCypher/'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"On3CGKelGEmb","executionInfo":{"status":"ok","timestamp":1716428982086,"user_tz":360,"elapsed":15506,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"be50082b-fbba-4ea8-c04c-4004a8ace023"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/MyDrive/finetuneCypher\n"]}]},{"cell_type":"code","source":["#@title Path Variables\n","\n","# Create a path variable for the data folder\n","data_path = '/content/drive/MyDrive/finetuneCypher/datas/'\n","\n","# SFT dataset\n","sft_data_file = 'parametric_trainer_with_repeats.json'\n","\n","# Create a path variable for the SFT model\n","sft_model_path = '/content/drive/MyDrive/finetuneCypher/cypherCodeLlama/'"],"metadata":{"id":"sB-2ytmoHBji","executionInfo":{"status":"ok","timestamp":1716439989418,"user_tz":360,"elapsed":168,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["#@title Hugging Face Credentials\n","\n","# For Hugging Face Hub setting\n","from huggingface_hub import login\n","\n","# Upload the HuggingFace token (should have WRITE access) from Colab secrets\n","HF = userdata.get('HF')\n","\n","# This is needed to upload the model to HuggingFace\n","login(token=HF,add_to_git_credential=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XA8PsahUF7Qv","executionInfo":{"status":"ok","timestamp":1716439981464,"user_tz":360,"elapsed":822,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"ada17855-ee75-49d5-bedf-96cc570b905b"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["Token is valid (permission: write).\n","Your token has been saved in your configured git credential helpers (store).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}]},{"cell_type":"markdown","source":["# Preprocess Datasets"],"metadata":{"id":"EKWsHZ4oVMQ2"}},{"cell_type":"markdown","source":["## SFT Dataset\n","\n","The supervised fine-tuning dataset is expected to have the following format:\n","\n","`messages: A list of dicts in the form {\"role\": \"{role}\", \"content\": {content}}`."],"metadata":{"id":"jVomLAsyVhJh"}},{"cell_type":"code","source":["#@title Load SFT Dataset\n","\n","import json\n","with open(data_path+sft_data_file, 'rb') as f:\n","\tsft_sampler = json.load(f)\n","\n","# Display an entry\n","sft_sampler[123]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pZ6JIKCuVRlL","executionInfo":{"status":"ok","timestamp":1716429042009,"user_tz":360,"elapsed":569,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"915f6483-1c61-4884-ef52-a1a1d03c24d3"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Prompt': 'Convert the following question into a Cypher query using the provided graph schema!',\n"," 'Question': 'Fetch the Author nodes and extract their affiliation property!',\n"," 'Schema': 'Graph schema: Relevant node labels and their properties (with datatypes) are:\\nAuthor {affiliation: STRING}',\n"," 'Cypher': 'MATCH (n:Author) RETURN n.affiliation'}"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["#@title Conversational Format Converter\n","\n","system_message = \"\"\"\n","You are a text to Cypher query translator. {prompt}\\n{schema}\n","\"\"\"\n","\n","# Function to transform the data to conversational format {role:, content: }\n","def create_conversation(sample):\n","    return {\n","        \"messages\": [\n","            {\"role\": \"system\",\"content\": system_message.format(prompt=sample[\"Prompt\"], schema=sample[\"Schema\"])},\n","            {\"role\": \"user\", \"content\": sample[\"Question\"]},\n","            {\"role\": \"assistant\", \"content\": sample[\"Cypher\"]}\n","        ]\n","    }"],"metadata":{"id":"ayw3jOVtWqH9","executionInfo":{"status":"ok","timestamp":1716429053786,"user_tz":360,"elapsed":201,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["#@title Convert Data to HuggingFace Format\n","\n","from datasets import load_dataset, Dataset\n","\n","sft_dataset = Dataset.from_list(sft_sampler)\n","sft_dataset.shuffle()#.select(range(100))\n","\n","# Transform to the required format\n","sft_dataset = sft_dataset.map(create_conversation,\n","                      remove_columns=sft_dataset.features,\n","                      batched=False)\n","\n","print(sft_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118,"referenced_widgets":["2d892824208a4fb78d3ecc0598667457","f6a5257378144263a19cc392819796fe","634a46a5a7a34301a3f26c78e05abf4e","e615c2947c5647599e22ae778065d003","b2d7fa52916642a3aa6e8709606bdcfe","3b69542521934f9893c0bfada0dec642","f156c1b1c8a64705b82b8651de46b44b","d3ddfbed164242279adb22a82981e890","9aac9ba327d64ccdb6cedc12256eb21b","ebfdd42036c34f01810491f2f969c097","823d38358a3b4de3958f6c4476983f07"]},"id":"fY33zZqVWGnu","executionInfo":{"status":"ok","timestamp":1716429067993,"user_tz":360,"elapsed":3325,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"08708365-4414-4680-d648-9d4c802a1131"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/30116 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d892824208a4fb78d3ecc0598667457"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['messages'],\n","    num_rows: 30116\n","})\n"]}]},{"cell_type":"code","source":["#@title Split Data into Train and Test Sets\n","\n","sft_dataset = sft_dataset.train_test_split(test_size=10/100)\n","\n","print(sft_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BlLR7wSfX_Lp","executionInfo":{"status":"ok","timestamp":1716429080378,"user_tz":360,"elapsed":103,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"1af55aaa-190c-4cdc-b0a1-f033b514b359"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['messages'],\n","        num_rows: 27104\n","    })\n","    test: Dataset({\n","        features: ['messages'],\n","        num_rows: 3012\n","    })\n","})\n"]}]},{"cell_type":"code","source":["#@title Display a sample\n","\n","sft_dataset[\"train\"][3][\"messages\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1jUvn914XdvK","executionInfo":{"status":"ok","timestamp":1716429089217,"user_tz":360,"elapsed":205,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"c1120188-9cd6-4416-c2e8-449b2f897080"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'content': \"\\nYou are a text to Cypher query translator. Convert the following question into a Cypher query using the provided graph schema!\\nGraph schema: Relevant node labels and their properties  are:\\nArticle\\nKeyword\\n\\nRelevant relationships are:\\n{'start': Article, 'type': HAS_KEY, 'end': Keyword }\\n\",\n","  'role': 'system'},\n"," {'content': ' Which nodes are connected to Article, but not to Keyword via HAS_KEY?',\n","  'role': 'user'},\n"," {'content': 'MATCH (c:Article)-[r]-(n) WHERE NOT (n)-[:HAS_KEY]-(:Keyword) RETURN labels(n)',\n","  'role': 'assistant'}]"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["#@title  Save SFT Preprocessed Data\n","\n","sft_dataset[\"train\"].to_json(data_path+\"sft_train_dataset.json\", orient=\"records\")\n","sft_dataset[\"test\"].to_json(data_path+\"sft_test_dataset.json\", orient=\"records\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":98,"referenced_widgets":["8eac7deddde745dd9bbb75f465fd6547","6aa538493a104139ae6d70c4a47e6b3b","56327dc3d4d842409731f150585ccdf1","fb5923e470fb4d62b805706d6341b8fc","3b0b7f0d66864697b591c8cef408d35c","5b7820f5f75e4cfdaec2bbe53c4b2845","424727ad42a04303b8aa9adae0e5e587","be68400f54074f66b9a94eb521cbd234","4ce80d9f7acf45b88c78ffbb04b98522","a16bebf2a67048208bac4b725f18cba1","8e6063cf9a5f4f2ba40b43cdebd88e0f","b30f6cc957c443908cb634d6b0b8105d","98efda4fe22c41fc8cd97bda4ab55857","2bbb39b9914f4b0ba87dea363e14edab","7284145870b64761b02644806eb23a5e","7673a76f7168444e9be17daa82407b1a","538c48a0c51c4f639fe16683db66ef5d","9e701d352a764f4794c96c54d5396d23","190823906cf041b89604391b5091ed23","4a9c89e894d5422983f727c3f4c7be80","7f61bf96990342ee9abb3a75b885add0","c222747900f74c54a3210df9cca2c0d8"]},"id":"NOeidbmlXkfZ","executionInfo":{"status":"ok","timestamp":1716429098932,"user_tz":360,"elapsed":2083,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"a67f4323-f92f-4c0f-a201-2d95d68d7f18"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["Creating json from Arrow format:   0%|          | 0/28 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8eac7deddde745dd9bbb75f465fd6547"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Creating json from Arrow format:   0%|          | 0/4 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b30f6cc957c443908cb634d6b0b8105d"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["2215660"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["# Run SFT"],"metadata":{"id":"DK4PH1T7i9io"}},{"cell_type":"code","source":["#@title Load Preprocessed Data\n","\n","from datasets import load_dataset\n","\n","# Load jsonl data from disk\n","sft_train_dataset = load_dataset(\"json\",\n","                       data_files=data_path+\"sft_train_dataset.json\",\n","                       split=\"train\")\n","sft_test_dataset = load_dataset(\"json\",\n","                       data_files=data_path+\"sft_test_dataset.json\",\n","                       split=\"train\")"],"metadata":{"id":"SYGbecc6YaEW","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["008638870988428184f8c41da79da3ec","d9b9a3acd52f4e6f9909f8c81879e39e","66c7c738640e46a3bd27c741bc408502","300c6ec050ac4f25a3db9b61194c9ee0","a24048c3e5d949bfac5d6377af14ce9f","d8278efc425e4405ae170391c7d1d2a9","33b5447658ac4b31b93bf8c1e66eb86a","dac73b4f214f436d99c90f466ad1f8f9","236b61ae0d5d410f8cdfbf4e895c477b","498c18735880489d8394cc8c2392463a","7a5bec45e20d458484cdbf8dcf2e18b4","7f8d9e4ce0c64ca68813d2c29f5555de","5dceb07b865d4e3788ceb68518696ea8","826bb7690f2144feb0e7d42d881d4158","dd1372d688a949d4a5f67de8d6c32853","333ff2b62172486c86c3756e157cf03e","30a6724ebce64dea9dfe6c367d991b60","6bd7bb4dad5343f1b800963a7e331345","6e853cb93ccd40128acb0874c8f12e38","e496b17f87594d03a46a68ecb6cb0580","f8552c032fbe4c10bbc32bd80ac80194","46cf2f758585405e954e896249045bc2"]},"executionInfo":{"status":"ok","timestamp":1716429106871,"user_tz":360,"elapsed":825,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"2bfd8d5c-205d-407b-9307-2618f221a6e6"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"008638870988428184f8c41da79da3ec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f8d9e4ce0c64ca68813d2c29f5555de"}},"metadata":{}}]},{"cell_type":"code","source":["#@title The Base Model\n","\n","model_name_or_path =  \"codellama/CodeLlama-13b-hf\""],"metadata":{"id":"1Ap_BBbWjNt2","executionInfo":{"status":"ok","timestamp":1716429116139,"user_tz":360,"elapsed":119,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["#@title Load Tokenizer\n","\n","from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,\n","                                          trust_remode_code=True)\n","# Choose padding side \"right\" to prevent warnings\n","tokenizer.padding_side = 'right'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269,"referenced_widgets":["807e69724c4f430c82533e56e2ad9aef","f9f08370edf94413b447270f5dcacf90","96793928266c4b59a0e9067365ad3daf","cd529bf5d28344fa916b5071cc18ce90","33732098e86a43e495a7a3fb0ab8fffe","2068f38912094e5eba1f0251090c3c4e","04c1a886eec146fc9f429cbe06678237","33d44285cc9a4e098903d71ca3bd8330","020c91877d5d474dbc918f303ead3fa2","43d9e944bc0b44908ad0514da447a6e5","1952ced035f544d088b6d2ce830a93a8","23e76fe17af94efd8b86b327d13d70e1","9f5ba5a1c79947e99d0e943a06e681cf","aebed8f7d204433ba687b777be76a73d","c8be24e0a159481e8539e7ec1526f8f0","717e5e2e66ba40568931fdcf33a092f9","270b357329f9406daf76263a56f3c047","a5e2890e648947c1aadd8ded88281ca9","a4e7769a1f654d3b876a6623f9239fa9","2703d4fc13bb4621b28ec46f08077d36","76a6034ccb0544f6918075b46face9a7","08c670bd3a534433a089989ad191cca1","1f7d16baf8264b6c9d00ecd5ef234a43","4491ee3bba444437bf3e6a299123c20c","ae787a5a61164aeeb58cc339955fb3b2","c59032860b60424eade13f50117fe767","cd1dc4a36e4c4c9488935362baf250fe","3123458e991b4ebcba3c4bdcf79d5ddd","fff46dea4af74119833d5c669f354234","167d28cc41ba4122ab675261ceeaedfc","c59f71cb9e5c4a19ba071b56869b7b09","19667af4d8f9497ab0c0600c58df913b","b1bb1efb8d2141848a1f6d7fc104dcee","44e829f899f146868da819c3b059e394","0980855af5af4329a4a920337720ae13","735229cf326445ef870607f378025a41","5fe11be858c540fc9a12a18a8da3df14","7ca18ccdd39845af8331ca629dd56275","4c000ca10e4446e5b411782d2a495844","fd5b8665e83a414b8ed7d3919a195d01","968dcc48ccf347c683f67a01c960a47b","d898f1f00ad04339b61470b1d721aa07","78a3363137834344a98583cc5760bb96","d5ecdee977a34c9b9fcb4dfa5fcdebde"]},"id":"66TVyLtakl-4","executionInfo":{"status":"ok","timestamp":1716429130260,"user_tz":360,"elapsed":1598,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"e42e0bf7-e739-4939-8b74-d7fe191a0823"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/749 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"807e69724c4f430c82533e56e2ad9aef"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"23e76fe17af94efd8b86b327d13d70e1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f7d16baf8264b6c9d00ecd5ef234a43"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"44e829f899f146868da819c3b059e394"}},"metadata":{}}]},{"cell_type":"code","source":["#@title Quantization Parameters\n","\n","from transformers import BitsAndBytesConfig\n","\n","bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_use_double_quant=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16\n",")\n"],"metadata":{"id":"ZNWX4b3bl4lc","executionInfo":{"status":"ok","timestamp":1716429153739,"user_tz":360,"elapsed":218,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["#@title Device Map\n","\n","device_map = {\"\": torch.cuda.current_device()} if torch.cuda.is_available() else None"],"metadata":{"id":"O6LTSSytvBGl","executionInfo":{"status":"ok","timestamp":1716429172038,"user_tz":360,"elapsed":306,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["#@title Load Base Model\n","\n","from transformers import AutoModelForCausalLM\n","\n","model = AutoModelForCausalLM.from_pretrained(\n","    model_name_or_path,\n","    revision=\"main\",\n","    device_map=device_map,\n","    attn_implementation=\"flash_attention_2\",\n","    torch_dtype=torch.bfloat16,\n","    quantization_config=bnb_config\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273,"referenced_widgets":["0407db0e3d4e42a5a96b4828bb77f955","601bd2e11a3e427e934c07bb9da0ea9e","ff8e195a7d3c4888842ee3cb8ae2d170","ffe40213f91a4d6a9fccb1ee9b61d03c","22703c5f49c74710be9e05f50026273b","815fc854c2c74d8182b74aae2fd04fef","2eddfb3f4173422b991b0c3032783bea","63581c7b11ec44f9bfce79c6edae92a2","3797f68ec90b4f2f86f517d6dcefb030","10e65f5e4c79421aaa4dbb0db34d4b6c","61f035fd9c52494d9a91a6fd384fa27c","c8787b4284574fc298aeb06cc469bb01","2e3890bc7d434153b5144039d427488f","11c77af2d56146e9a9f4a5ceb75116a1","58b60cab13a743178bfc574fd59a38b7","57466d2958574794bf1649a7b0f4752c","6f8becd49ceb4d47b0fa3f78b39012ae","79858131dc174971b8d35a0b911daa0d","2cee44b5f21645439388fe18959a6ead","100711f1eb1c4293b133b8da46d62a39","9fc8c9f582ab4a43ab03fe6e22e4328d","6601298bc0304b928865dc095a780eb6","2bfcb702af444dcb8c965ce29a9f90a5","388bb4e61c3b4ccb80b2e03957592f3f","c0e4e2b7b479485ba19e5ea4960e11d7","ff698cd8a1c5471cb1830aaa6f36b275","5d364165e23841b0b6c6c88fdc5d71f1","0ce15e0c34a84c0797e4d029dc102128","023c5c4b62ad41148fd8f778844dcff5","c55cd6c739264f6abe5a522c1884674b","568f3bf2e97342f4ae42aa61fab897f0","d573cbe329cf4fa2b0c7d4cf8789dca6","b514949973404d02a908495b7bff7836","708e24c09bfd4877b9a72baae4e27722","b785e3d2484e4e279ab097f3af827ed0","10c449cc00554235af954f484b406631","6562cf784f0b4409bfd98665ac7e363f","cc805f06c17f439299f586c82e0974d4","a9f547b237b24982bf08040c9cd7b177","7227ae7da3784a2fac2e9a426885f887","656c4a22988f4f4bb1f70bccdf5f467d","7b63e83581dd490194e3cda2de75f0f2","1850ba59d5ed4723bb22b1a3c061b2a6","f7c81a4d193d4c0489b840d4f46f0f94","63b7bb5a100c400d850f1446e58c62b8","8c5e65b0f912410393968ae38c1c5545","e3060ad8d1594860b377fc1e2d8d22f0","ccc66df728864084adb2c8a2bb0d07cc","c8cc5b3242784d8ebf34fa8b33ed6626","a9146cffa58741f9b930471376c79ebc","d2e82e0d3afa495292cd6b62064c1987","f299f6c3454f4453b0c8c9ff70c61270","11fcf117d3094ca696b78064b7f66b7a","3e15ce892c224a559246f8d64e5f8ca3","67ef5e538ae7495b9fa4f3cccf581c81","ddbd3d1535e94aa3a5cd33086255d09d","286ba41f3a0348628ccd5e976e0b8171","aa0a410a2183461f9cb3e947ae2e3e93","6bef7cb6afa44e8993b74c673f30845e","ba5b0e1c73db41a6b3fc6a55e85a4003","977ae69b869848748e845f57b72cc7bc","da935e37a3484e56859a030f62bee31b","cd7cac0223c2458f95dd16e41dc2a6b3","cc866e44ee574fa8a87f4f10d4fd131a","908853160dea4a7c96e8103f3244ceff","b35cc473f8604c08bd1fb42ffe6b8f47","4642afc5b63f473b9bd9a459a4bdcbbc","3b8081fcd0fd4b43af05207736ff28ee","99c54f2c978e48239aca24ba841b2227","c80bf677ccaf4f0eaaf66a2b1127cba6","0130f0168c3b451889aec8a0b879712c","9b38e257787945ad902635ab3c3c0cb4","4d7bf1e100864a60b9f815f0739def92","0046e2ff03e64deea600b66719e9c61d","8498b1ef766b48288c7af94e5b5e9857","7f33ae2e53584650a97d5ca6f15a52b0","0a8a594eb22748dc97d67034c0cf7e50","432b7b59a39943ca82c5300d43c1d1b1","5906446e60824e88ae043da6811b0814","1bc68555a30b41d1acdc4772c87d2ee3","68398b0d10764594a126ed38f7fc1b28","2e0a7a78d9b04beb9f1848fd258d8118","c3c4ad224ee64b1eb5edfeea57a40860","87f3f3b986a4400ab60a909789ddc9ca","8041446b6b814bdd917747e1cac2a669","96e65ceb28484bc8a1fbd980283c4b11","73b2d6a60a5d4d7cb277a2622e794299","b1488acfb19d4af7a3d9dfdfdf0296ba"]},"id":"ezJADVaMma_f","executionInfo":{"status":"ok","timestamp":1716429377855,"user_tz":360,"elapsed":174729,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"cd04972a-fe6b-4c91-cf6e-681a7028c184"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/589 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0407db0e3d4e42a5a96b4828bb77f955"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/31.4k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c8787b4284574fc298aeb06cc469bb01"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2bfcb702af444dcb8c965ce29a9f90a5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00003.safetensors:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"708e24c09bfd4877b9a72baae4e27722"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00003.safetensors:   0%|          | 0.00/9.90G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"63b7bb5a100c400d850f1446e58c62b8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00003.safetensors:   0%|          | 0.00/6.18G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ddbd3d1535e94aa3a5cd33086255d09d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4642afc5b63f473b9bd9a459a4bdcbbc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"432b7b59a39943ca82c5300d43c1d1b1"}},"metadata":{}}]},{"cell_type":"code","source":["#@title Set Chat Template\n","\n","from trl import setup_chat_format\n","\n","# Use the chat template ChatML by OpenAi builder from Hugging Face\n","model, tokenizer = setup_chat_format(model, tokenizer)"],"metadata":{"id":"WLtFwc0Km4eW","executionInfo":{"status":"ok","timestamp":1716429499086,"user_tz":360,"elapsed":851,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["#@title LoRA Configuration\n","\n","from peft import LoraConfig\n","\n","peft_config = LoraConfig(\n","        lora_alpha=16,\n","        lora_dropout=0.05,\n","        r=8,\n","        bias=\"none\",\n","        target_modules=[\"q_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",\n","                        \"k_proj\", \"v_proj\"], # apply LoRA to all linear layers\n","        task_type=\"CAUSAL_LM\",\n",")"],"metadata":{"id":"29N1BPN9wOTF","executionInfo":{"status":"ok","timestamp":1716429725382,"user_tz":360,"elapsed":120,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["# @title Training Arguments\n","\n","from transformers import TrainingArguments\n","\n","# Adapted from  Phil Schmid blogpost\n","args = TrainingArguments(\n","    output_dir=sft_model_path,              # directory to save the model and repository id\n","    num_train_epochs=1,                     # number of training epochs\n","    per_device_train_batch_size=8,          # batch size per device during training\n","    gradient_accumulation_steps=4,          # number of steps before performing a backward/update pass\n","    gradient_checkpointing=True,            # use gradient checkpointing to save memory, use in distributed training\n","    optim=\"adamw_8bit\",                     # use paged_adamw_8bit if not enough memory\n","    logging_steps=100,                      # log every 100 steps\n","    save_strategy=\"epoch\",                  # save checkpoint every epoch\n","    learning_rate=2e-4,                     # learning rate, based on QLoRA paper\n","    bf16=True,                              # use bfloat16 precision\n","    tf32=True,                              # use tf32 precision\n","    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n","    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n","    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n","    push_to_hub=True,                       # push model to Hugging Face hub\n","    hub_model_id=\"codellama-13b-sft-qlora-cypher\",\n","    report_to=\"tensorboard\",               # report metrics to tensorboard\n",")"],"metadata":{"id":"BxjgqvhuwVxU","executionInfo":{"status":"ok","timestamp":1716429961583,"user_tz":360,"elapsed":120,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["# @title Initialize the SFTTrainer\n","\n","from trl import SFTTrainer\n","\n","trainer = SFTTrainer(\n","    model=model,\n","    args=args,\n","    train_dataset=sft_train_dataset,\n","    peft_config=peft_config,\n","    max_seq_length=512,\n","    tokenizer=tokenizer,\n","    dataset_kwargs={\n","        \"add_special_tokens\": False,  # the template adds the special tokens\n","        \"append_concat_token\": False, # no need to add additional separator token\n","    }\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["c06be02eb5a14df7b7a88ea4bac82b0b","8dcd23eacbbc49bf9d4ebd5c2f3a5ad8","d20c775616784a7b9aea5a348670ec35","1c6abc6482c04acbb0d9ad48d3a53d38","128f044f851f4f8ba994227aa8c61987","fb1fa1800ae94e02912a16629d391b9c","55f6459270df49b488a68c5e07d708e2","2d9e79ce49694f9f80942c6b91d1cb21","f6d0803a13da42a09bbda4c7f43b1c51","fb6bda9a6cdd422c8d0bfd1713268c38","76c58a21e367409f892f1170c0efde09"]},"id":"8eIlh9p1w2XX","executionInfo":{"status":"ok","timestamp":1716430117126,"user_tz":360,"elapsed":5688,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"b22b8afd-6136-4dde-ba10-bce840be2e04"},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/27104 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c06be02eb5a14df7b7a88ea4bac82b0b"}},"metadata":{}}]},{"cell_type":"code","source":["#@title Train the Model\n","\n","trainer.train()"],"metadata":{"id":"OLD5-cVNxMIF","colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"status":"ok","timestamp":1716438191074,"user_tz":360,"elapsed":517834,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"4015d09f-7fae-4294-864c-0f57fb379c54"},"execution_count":30,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n","The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in torch.bfloat16.\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='793' max='847' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [793/847 2:04:36 < 08:30, 0.11 it/s, Epoch 0.94/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>100</td>\n","      <td>0.492300</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.151900</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.082700</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.067800</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.063800</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.057800</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.057200</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='847' max='847' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [847/847 2:13:09, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>100</td>\n","      <td>0.492300</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.151900</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.082700</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.067800</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.063800</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.057800</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.057200</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.056100</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:209: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=847, training_loss=0.12463992489270928, metrics={'train_runtime': 8000.4263, 'train_samples_per_second': 3.388, 'train_steps_per_second': 0.106, 'total_flos': 6.629534769905664e+17, 'train_loss': 0.12463992489270928, 'epoch': 1.0})"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["#@title Save Model Locally\n","\n","trainer.save_model()"],"metadata":{"id":"sJPsSOLGxPuL"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Inference Tests"],"metadata":{"id":"zwH2mGCWToAU"}},{"cell_type":"code","source":["#@title Clear Memory\n","import gc\n","#del trainer\n","del model\n","del tokenizer\n","gc.collect()\n","torch.cuda.empty_cache()"],"metadata":{"id":"OSngQbOzbAwZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Load Peft Model\n","\n","from peft import AutoPeftModelForCausalLM\n","from transformers import AutoTokenizer, pipeline\n","import torch\n","\n","# HF model\n","peft_model_id = \"solanaO/codellama-13b-sft-qlora-cypher\"\n","# Local model\n","#peft_model_id = sft_model_path\n","\n","# Load Model with PEFT adapter\n","model = AutoPeftModelForCausalLM.from_pretrained(\n","  peft_model_id,\n","  device_map=\"auto\",\n","  #torch_dtype=torch.float16,\n","  load_in_4bit=True\n",")"],"metadata":{"id":"iJnxWxUxbAtT","colab":{"base_uri":"https://localhost:8080/","height":656,"referenced_widgets":["b3aafefc4e1f4cb2bc198a6bdb5a7534","9e7aa999deab4f97a2e0c33e0da3add2","827fd2ad513840f997d884e051a63ba7","67ba69178b52439eaf94e8be8c07aefc","428f3dd43eb64041a75482b96e7c5b36","6a3d7c222e4f4215a8b22ecaf83f9dfd","8a5aecf751b64d65b9a9dbcd80dd17ce","4467fb411f98429b9ff1b3f8b4230270","7a4b31e9479646daae6fcfc3eb18ea09","a28c337491e341079d2b4065e59a98fc","352ce8e4ab65498f8cc8d0fb909d973f","8722b45608114f63bc4afa80a5980f5e","de0216ee16d74376adfba9a27848c9b5","6cb5d6c52976463db141e091da621ca2","2ca39a1de9684f6a9d06bb76d2f2905a","974a2774c2e646edb665e2ab4c098b1c","06c8466e67104bccae3e5fd329bd2683","27909a65c519455aafad5f4e9ffa89a4","f391a60184d64431a76cb87c843aa551","d7ee8b7518a148b3b43cefde5d82b05a","3cb61bb8eebb438ca5161db8d4e41f57","68ac0667dccf4136adfb30aca807a882","a655872464b643d99e09d39f9df97d7d","a973fdd859da47b5829e1ef8ed48cb14","c4757c3f8e9a41f6b84de37c71585a3e","da10d58856de42faa37f6017ec3821f1","ac2146901fc147dfb71e5b2c0e085e2d","beb7ec81cf8d49e49205b82c2664d0e8","ca99a51867844334a65f228563f28c5a","adeb2ddf19fb4cfa9118f4dde26a1314","b1814f058a5349f586ff90c9da370239","ea747c1edac34eb59bc27200820cd430","9733728bcf934eb8add32cb9b4fed35a","e06120dbf8704d55bf14b5e12615f905","a37a72ef412446a0871f087cc25501d6","a67db84349d24de788461f86ef0e6b6d","e5ffa7077a88496bb31ee65c843b23fd","bc7ba1cb4bb149a08492f925980600cf","eb6d8e3146d543e48c84aba1df3284eb","18095b32d7a140ae8a224b95961b1a53","347dc27ccae64cc8b246397d041a706e","cdad75867dfa40f8a4cad7deb7f5fcc2","98dca1aa9d71457697137ce68992b7ec","fce07090b7b04336987ee052d618034e","75d392b011ce4ee08a41197f325529cd","d6bcfa253bf042ad89ad6f33999ac623","fd1e4bc71bc041c0806fd7dedd6d6026","ec6192006c554cc99d571b68431f0667","3862f9117f7b4a078c67e6bc0a8977e2","b0d9f7e3ac9c45a088ea556c60f93dfc","6d1a1b2e531e48c7b458b9da07c9aff1","81647ea090b74563a45ae42872569ca5","c7be6ba31e6a43eabb2d4d04edb959aa","497d121557e94cfd8f82a581d2e054ea","347d81fa5fcf468abc39ae5e8e96718b","8de75fa51e4844338f2d4cd2a0451352","0186a9a984284b95a29c1ff803166fe7","d5b04701314b4c0b939e074680bc9eee","e8a4b3f2539a4ff7bfc8b159773ce93e","e86ede8d78814391be615ad58d0283a7","5a439ce24a6744448c4de668759fcc5e","6abbe2dc98ee4f48a5a28d4ee56d9c88","e28db64efc1443b4819a2a37ec75b348","7020ee7d01fd45b782002e35e5f875d7","498d81e6b6864e0db049aec7bbee0127","863ba44c70c044a38bafe3ef07447e0c","4a6920adc9524ef58362f6e278f2023f","8e40a6c628954b65917af6d7cf562da0","32d892281a0b4316afec646e70ce869c","0936435834de4a5691f9dd6dc5cfa5ed","4e4887d0d4764780b94f2b8cfefd0a69","978455b93e8946688d3d54b08c22b990","48470af6583c40d1860d8ce60704bead","64bfcdbf117a4d67a238dbeade4a6338","df57b9f6c70f41949d1623159aba494b","def00085cb364f6488dc0cdce746036f","673a3a20d28f43bcbf6e32d77aaea193","12b4067f87304ba7aa055d64d603344d","37abd635e19045ce87f18083b1a24f5d","69edc1be1c4e48539e4e3585714e4e56","43b9fab102e54f1e9095e5dd9d2b8da3","129faf13cda4461384b7bbb578346dac","8e1183ebaf0c4929bd1a575b7c1ee20b","c04445ba04f14d36803a9d45283b3f2f","c08bb255e7f84b9b9ae1a2af04e309c4","ada5da4bc8554d3ab4e4bde11392cdde","35e26cdd3bba431f9de282e92ce8a9f9","16c894c3e4ea4e48b9aa6078d9bda8da","f13adb2a88ba4b369e86888fe834f698","b223ead1d9ae44c2972f66537cf0cf0e","efdca5c6de3945b58c951194d7428907","6355b115a4b2479bafe27fa23d35bdf2","b4e35f71abf64483b807b62c1e1b7214","cd1debb320304157949628bb9e6b7bc5","000857824a7e45f69cbb048237beb7f3","007c6b1cceef4bf6a086b63b54832f16","4295073a5d7f4073be440194228f5b0a","1284092edf90470e891864f0102b4d17","be40129ea17b4ca7ad040acba7ff449b","b8afddf7ae8147928ecf7e9ed908e3fd","9f6eb2b60dab426eb5c69e36f3eea55b","7b08b00cce6e4fdb94f06344118dfbe6","d377ae1f485742869ca54c4977d4c88b","fb26dbba9d1a453c887a08c405f4d413","ffed638c8b0e4cea9f02ac583949e8b1","143848faad1e42d2b90feb9ecebd9519","cfbcbb174e7b419b8481eec9ad59eb80","24b4ecf06c3548abbc92e202170e19b1","8af0e8c901114da49939e38736a4326e","f32ba9bccc5b4baf8f6c66819e7ed6ec","142a59a012bf434083245c59e8833dd2","8534bab64ad240a88c9b6b301d8b4525","d3ddf8168e094b18bbb626825a22436d","d2516ab9ad574a73b756d76d5f780804","06a2a5b9fb334af788b0bb3f744ca50d","a492c7cc19b74caca6b4de31a6e6f766","4f06a5558fde4d17a74a5cc1bb1fb7e5","6d904b18a5d44db698763af6366c43b6","14bedc1adf344eb9955aa0d9dcd06cb0","1643b1ef73c248b29042fba52e3e26e3","f1fa6ad6dcee4a7da26f5ab4e49b3005","a1026981c0a44af6b65cd2f6f80ca7c9","934c6a70b0944fc386517d914ed99378","510f1a7df7a34bceb567756bb63cc8a3","74e19c64ce874e12aeb7a48c822b43f3","be38025b2f674cd3b675483cab13efee","d0ca30aae7d14bd297ee114d23f91054","f37fb8b068e44bc188f7e38cea2ea143","d1ab226c17144a32baf6a05a1924c6ce","95f2427c116f4eada84d4981dc5244ed","7e7a7c7b33434b5d81bc85253f642798","505761e275124dfd923eea91e4896cf4","d71c2299a82248bf9df7ab8e26385807","f1b23f75724643acbfb3c60daab596c6","000b2b34f2704ebc860999185e9f348a","80c67a7ccd4e436fa7aef8f216948353","79dd7ddde76f46f2adbac67160f78ffa","a4466a3b580a4f5fbe2117b39ff79c68","3f7c0d1cc44b48db9b2580ef65a203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Onofrei","userId":"08518681359279549405"}},"outputId":"cff9ba1b-5fcd-4dd7-ef88-ee225aab4036"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["adapter_config.json:   0%|          | 0.00/702 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b3aafefc4e1f4cb2bc198a6bdb5a7534"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/589 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8722b45608114f63bc4afa80a5980f5e"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/31.4k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a655872464b643d99e09d39f9df97d7d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e06120dbf8704d55bf14b5e12615f905"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00003.safetensors:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"75d392b011ce4ee08a41197f325529cd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00003.safetensors:   0%|          | 0.00/9.90G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8de75fa51e4844338f2d4cd2a0451352"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00003.safetensors:   0%|          | 0.00/6.18G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4a6920adc9524ef58362f6e278f2023f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"12b4067f87304ba7aa055d64d603344d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f13adb2a88ba4b369e86888fe834f698"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/2.44k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b8afddf7ae8147928ecf7e9ed908e3fd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"142a59a012bf434083245c59e8833dd2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1026981c0a44af6b65cd2f6f80ca7c9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/51.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d71c2299a82248bf9df7ab8e26385807"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/557 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5e6625c6f6a644bbab25012558098504"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]},{"output_type":"display_data","data":{"text/plain":["adapter_model.safetensors:   0%|          | 0.00/2.66G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fcf686f2d78e458abba4f68d1cc89cd5"}},"metadata":{}}]},{"cell_type":"code","source":["#@title Load Tokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(peft_model_id)\n","model.resize_token_embeddings(len(tokenizer))"],"metadata":{"id":"67H1BSR6U48g","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710386938042,"user_tz":360,"elapsed":923,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"84868e6c-4229-4a30-ca71-32f8397f2901"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]},{"output_type":"execute_result","data":{"text/plain":["Embedding(32018, 5120)"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["# Text generation pipeline\n","pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)"],"metadata":{"id":"JSdpPOKsU6Pc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710386944061,"user_tz":360,"elapsed":673,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"1dfbb768-c449-4ba6-ee5f-562cd9e994bf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'LlamaForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'FuyuForCausalLM', 'GemmaForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'MptForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'Qwen2ForCausalLM', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'StableLmForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'WhisperForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"]}]},{"cell_type":"code","source":["#@title Load Test Data\n","\n","from datasets import load_dataset\n","from random import randint\n","\n","# Load jsonl data from disk\n","eval_dataset = load_dataset(\"json\", data_files=data_path+\"sft_test_dataset.json\", split=\"train\")\n","rand_idx = randint(0, len(eval_dataset))"],"metadata":{"id":"N7QBOGQsbAqX","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["b887c492def8466da63f525a636a672e","8e4456563dce42bcb357a291c6be9dcb","e9d4f26d9b104c2caacaec8036b96a4d","77c7aa7a62644aa4bb7e10b7aa6c2c08","d4b9cdab26cd4ea7b61bac2f91ad9718","680574ccc72b449dbb1357de235b4fc0","36cea492818a487c82f24834df64811d","9d32af29e6be40fa8adaec64ccf967af","c043277fbfb141078f3888540487169e","4cdad26db5784f85b8b016699a6a6ae2","0adff6f8d11e409a855611138ce03fc7"]},"executionInfo":{"status":"ok","timestamp":1710386951338,"user_tz":360,"elapsed":3515,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"e8341dab-daff-4f37-e624-2830bafa3b04"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b887c492def8466da63f525a636a672e"}},"metadata":{}}]},{"cell_type":"code","source":["#@title One Sample Test\n","\n","prompt = pipe.tokenizer.apply_chat_template(eval_dataset[rand_idx][\"messages\"][:2],\n","                                            tokenize=False,\n","                                            add_generation_prompt=True)\n","print(prompt)"],"metadata":{"id":"TsZdMvJZbAne","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710386956013,"user_tz":360,"elapsed":617,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"61716ee3-8a28-4b9f-bbbf-5b980bf813c5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<|im_start|>system\n","\n","You are a text to Cypher query translator. Convert the following question into a Cypher query using the provided graph schema!\n","Graph schema: Node properties are the following:\n","Article {abstract, article_id, comments, title}\n","The relationships are the following:\n","(:Article)-[:HAS_KEY]->(:Keyword),(:Article)-[:HAS_DOI]->(:DOI),(:Article)-[:HAS_CATEGORY]->(:Categories),(:Article)-[:WRITTEN_BY]->(:Author),(:Article)-[:UPDATED]->(:UpdateDate),(:Article)-[:PUBLISHED_IN]->(:Journal),(:Article)-[:HAS_REPORT]->(:Report)\n","<|im_end|>\n","<|im_start|>user\n","Retrieve distinct values of the title and the comments from Article where title is not The Gervais-Neveu-Felder equation for the Jordanian quasi-Hopf\n","  U_{h;y}(sl(2)) algebra!<|im_end|>\n","<|im_start|>assistant\n","\n"]}]},{"cell_type":"code","source":["outputs = pipe(prompt,\n","              max_new_tokens=150,\n","              do_sample=True,\n","              temperature=0.1,\n","              top_k=50,\n","              top_p=0.1,\n","              eos_token_id=pipe.tokenizer.eos_token_id,\n","              pad_token_id=pipe.tokenizer.pad_token_id\n","              )"],"metadata":{"id":"71nwML1ibAj-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(f\"Question: {eval_dataset[rand_idx]['messages'][1]['content']}\")\n","print(f\"Corect Cypher: {eval_dataset[rand_idx]['messages'][2]['content']}\")\n","print(f\"Generated Cypher: {outputs[0]['generated_text'][len(prompt):].strip()}\")"],"metadata":{"id":"BP-UMgxrbAgX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710387071415,"user_tz":360,"elapsed":292,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"248b242d-de9f-4909-c1c4-b4cfce6686cf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Question: Retrieve distinct values of the title and the comments from Article where title is not The Gervais-Neveu-Felder equation for the Jordanian quasi-Hopf\n","  U_{h;y}(sl(2)) algebra!\n","Corect Cypher: MATCH (n:Article) WHERE n.title <> 'The Gervais-Neveu-Felder equation for the Jordanian quasi-Hopf\n","  U_{h;y}(sl(2)) algebra' RETURN DISTINCT n.title AS title, n.comments AS comments\n","Generated Cypher: MATCH (n:Article) WHERE n.title <> 'The Gervais-Neveu-Felder equation for the Jordanian quasi-Hopf\n","  U_{h;y}(sl(2)) algebra' RETURN DISTINCT n.title AS title, n.comments AS comments\n"]}]},{"cell_type":"code","source":["#@title Test on 100 Samples\n","\n","from tqdm import tqdm\n","\n","def evaluate(sample):\n","    prompt = pipe.tokenizer.apply_chat_template(sample[\"messages\"][:2],\n","                                                tokenize=False,\n","                                                add_generation_prompt=True)\n","    outputs = pipe(prompt,\n","                   max_new_tokens=256,\n","                   do_sample=True,\n","                   temperature=0.7,\n","                   top_k=50,\n","                   top_p=0.95,\n","                   eos_token_id=pipe.tokenizer.eos_token_id,\n","                   pad_token_id=pipe.tokenizer.pad_token_id)\n","\n","    predicted_answer = outputs[0]['generated_text'][len(prompt):].strip()\n","\n","    if predicted_answer == sample[\"messages\"][2][\"content\"]:\n","        return 1\n","    else:\n","        return 0\n","\n","success_rate = []\n","number_of_eval_samples = 100\n","\n","# Iterate over eval dataset and predict\n","for s in tqdm(eval_dataset.shuffle().select(range(number_of_eval_samples))):\n","    success_rate.append(evaluate(s))\n","\n","# compute accuracy\n","accuracy = sum(success_rate)/len(success_rate)\n","\n","print(f\"Accuracy: {accuracy*100:.2f}%\")"],"metadata":{"id":"MIoRpNw4bAaR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710388003940,"user_tz":360,"elapsed":893199,"user":{"displayName":"Silvia Onofrei","userId":"08518681359279549405"}},"outputId":"e940c3ec-470f-45d4-a896-e996e8bcae23"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["  8%|▊         | 8/100 [01:10<16:07, 10.52s/it]/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1157: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n","  warnings.warn(\n","100%|██████████| 100/100 [14:52<00:00,  8.93s/it]"]},{"output_type":"stream","name":"stdout","text":["Accuracy: 86.00%\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"6z_Pe08jbATA"},"execution_count":null,"outputs":[]}]}